,

where – is a non-decreasing -order-convex function on a partially set .

Let be an optimal solution of Problem A, and let be the point obtained by the following iterative procedure [4]:

which halts on the step if either or is the maximal element of the set (the set contains the zero , as we have stipulated). This point is called the gradient maximum os the function on the set [4].

By a guaranteed error estimate for the gradient algorithm in Problem A we mean a number

.

By perturbations of problem A by means problem B

,

where is a non-decreasing -order-convex function on a partially set and .

Let be a guaranteed error estimate for the gradient algorithm in some unperturbed (perturbed) discrete optimization problem. As usual (see. [3]), we say that the gradient algorithm is stable if , where as .

Theorem. Let and be guaranteed error estimates for the gradient algorithm in Problems A and B, respectively. Then .

To prove Theorem, we need the following lemma.

Lemma. The gradient maximum and the global maximum of any -ordered-convex non-decreasing function on are connected by the following relations:

, (1)

where

– is the set of all maximal elements of the partially ordered set .

Proof of Lemma. By virtue of item of Theorem 4 [4], we have for

Together with the fact that

the last inequality yields

.

Therefore

,

Where

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H e n ( x ) = ( − 1 ) n e x 2 2 d n d x n e − x 2 2 = ( x − d d x ) n ⋅ 1 , {\displaystyle {\mathit {He}}_{n}(x)=(-1)^{n}e^{\frac {x^{2}}{2}}{\frac {d^{n}}{dx^{n}}}e^{-{\frac {x^{2}}{2}}}=\left(x-{\frac {d}{dx}}\right)^{n}\cdot 1,} {\displaystyle {\mathit {He}}_{n}(x)=(-1)^{n}e^{\frac {x^{2}}{2}}{\frac {d^{n}}{dx^{n}}}e^{-{\frac {x^{2}}{2}}}=\left(x-{\frac {d}{dx}}\right)^{n}\cdot 1,}

while the “physicists’ Hermite polynomials” are given by

H n ( x ) = ( − 1 ) n e x 2 d n d x n e − x 2 = ( 2 x − d d x ) n ⋅ 1. {\displaystyle H_{n}(x)=(-1)^{n}e^{x^{2}}{\frac {d^{n}}{dx^{n}}}e^{-x^{2}}=\left(2x-{\frac {d}{dx}}\right)^{n}\cdot 1.} {\displaystyle H_{n}(x)=(-1)^{n}e^{x^{2}}{\frac {d^{n}}{dx^{n}}}e^{-x^{2}}=\left(2x-{\frac {d}{dx}}\right)^{n}\cdot 1.}

These two definitions are not exactly identical; each is a rescaling of the other:

H n ( x ) = 2 n 2 H e n ( 2 x ) , H e n ( x ) = 2 − n 2 H n ( x 2 ) . {\displaystyle H_{n}(x)=2^{\frac {n}{2}}{\mathit {He}}_{n}\left({\sqrt {2}}\,x\right),\quad {\mathit {He}}_{n}(x)=2^{-{\frac {n}{2}}}H_{n}\left({\frac {x}{\sqrt {2}}}\right).} {\displaystyle H_{n}(x)=2^{\frac {n}{2}}{\mathit {He}}_{n}\left({\sqrt {2}}\,x\right),\quad {\mathit {He}}_{n}(x)=2^{-{\frac {n}{2}}}H_{n}\left({\frac {x}{\sqrt {2}}}\right).}

These are Hermite polynomial sequences of different variances; see the material on variances below.

The notation He and H is that used in the standard references.[5] The polynomials Hen are sometimes denoted by Hn, especially in probability theory, because

1 2 π e − x 2 2 {\displaystyle {\frac {1}{\sqrt {2\pi }}}e^{-{\frac {x^{2}}{2}}}} {\frac {1}{\sqrt {2\pi }}}e^{-{\frac {x^{2}}{2}}}

is the probability density function for the normal distribution with expected value 0 and standard deviation 1.

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